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• W2886516610 abstract "Comfort oriented control approaches require detailed knowledge of the spatial room conditions, especially how the temperature is affected by diverse air conditioning and ventilation operation. This paper presents different modeling approaches based on Gaussian Process Regression for the prediction of the temperature field using the example of an open-plan office equipped with randomly distributed sensors. In addition to a common location-based modeling approach, a distance-based model is introduced. Furthermore, ventilation condition, as well as additional boundary conditions, are taken into account. The investigated models are compared in regard to their predictive quality and ability to adapt to changing air conditioning operation. A CFD simulation is used as a reference for the evaluation of the model's quality. It is shown that the application of a Gaussian process model provides a detailed knowledge of the spatial temperature distribution and allows for prediction of the impact of diverse ventilation conditions, such that disturbance compensation and local actuation of temperatures can be enabled for future control concepts." @default.
• W2886516610 created "2018-08-22" @default.
• W2886516610 creator A5036338282 @default.
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• W2886516610 date "2018-06-01" @default.
• W2886516610 modified "2023-09-24" @default.
• W2886516610 title "Versatile Ventilation and Air Conditioning Operation: Gaussian Process Regression for Temperature Field Prediction" @default.
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• W2886516610 doi "https://doi.org/10.23919/acc.2018.8431040" @default.
• W2886516610 hasPublicationYear "2018" @default.
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